Lead Deep Learning Engineer (5+ years | Bangalore)

At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.

Responsibilities:

• Research, design, develop, implement and test econometric, statistical, optimization and machine learning models.
• Design, write and test modules for Nielsen analytics platforms using Python, R, SQL and/or Spark.
• Utilize advanced computational/statistics libraries including Spark MLlib, Scikit-learn, SciPy, StatsModels or R. 
• Collaborate with cross functional Data Science, Product, and Technology teams to integrate best practices from across the organization
• Provide leadership and guidance for the team in the of adoption of new tools and technologies to improve our core capabilities
• Execute and refine the roadmap to upgrade the modeling/forecasting/control functions of the team to improve upon the core service KPI’s
• Ensure product quality, stability, and scalability by facilitating code reviews and driving best practices like modular code, unit tests, and incorporating CI/CD workflows 
• Explain complex data science (e.g. model-related) concepts in simple terms to non-technical internal and external audiences 

Key Skills:

• 5+ years of professional work experience in Statistics, Data Science, and/or related disciplines, with focus on delivering analytics software solutions in a production environment
• Strong programming skills in Python with experience in NumPy, Pandas, SciPy and Scikit-learn.
•  Hands-on experience with deep learning frameworks (PyTorch, TensorFlow, Keras).
• Solid understanding of Machine learning domains such as Computer Vision, Natural Language Processing and classical Machine Learning.
• Proficiency in SQL and NoSQL databases for large-scale data manipulation
• Experience with cloud-based ML services (AWS SageMaker, Databricks, GCP AI, Azure ML).
• Knowledge of model deployment (FastAPI, Flask, TensorRT, ONNX) MLOps tools (MLflow, Kubeflow, Airflow) and containerization.

Preferred skills:

• Understanding of LLM fine-tuning, tokenization, embeddings, and multimodal learning.
• Familiarity with vector databases (FAISS, Pinecone) and retrieval-augmented generation (RAG).
• Familiarity with advertising intelligence, recommender systems, and ranking models.
• Knowledge of CI/CD for ML workflows, and software development best practices.

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